Capturing Key Attributes of Fine-Grained Sedimentary Rocks In Outcrops, Cores, and Thin Sections: Nomenclature and Description Guidelines
Bibliographic record
Abstract
Abstract: An integrated nomenclature scheme is proposed to capture the inherent heterogeneity of fine-grained sedimentary rocks at the 102 to 10−3 mm scale and to assist the evaluation of these rocks as sinks of organic carbon, barriers to fluid flows, and reservoirs of oil and gas. This scheme incorporates previous knowledge and the latest field, petrographic, and laboratory observations. We propose to name fine-grained sedimentary rocks using a root term based on their texture (grain size), which is modified by description of bedding, composition, and grain origin. Regarding texture, we suggest the use of “mudstone” as a class name for the entire spectrum of fine-grained sedimentary rocks. We define mudstone as a rock in which more than fifty percent of its grains are mud (clay and silt) size (< 62.5 µm). Similar to the approach used for the description of sandstone texture, mudstone texture can be refined by a “coarse,” “medium,” or “fine” size-range term. Regarding bedding, we follow Campbell's (1967) genetic approach to define laminae, laminasets, and beds, and describe lamina geometry, continuity, and shape. Regarding composition, we propose terms such as “siliceous,” “calcareous,” “argillaceous,” and “carbonaceous” to capture differences in rock composition. The name of a mudstone can be further modified by additional attributes that detail the form and origin of the rock components. Application of this approach to the Cretaceous Eagle Ford Shale illustrates the variability typically present in mudstone successions and demonstrates how our detailed characterization can be used to decipher and predict rock properties of economic interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".